Chapter 9 - New Developments: Topic Modeling with BERTopic!
Contents
Chapter 9 - New Developments: Topic Modeling with BERTopic!#
2022 July 30

What is BERTopic?#
As part of NLP analysis, it’s likely that at some point you will be asked, “What topics are most common in these documents?”
Though related, this question is definitely distinct from a query like “What words or phrases are most common in this corpus?”
For example, the sentences “I enjoy learning to code.” and “Educating myself on new computer programming techniques makes me happy!” contain wholly unique tokens, but encode a similar sentiment.
If possible, we would like to extract generalized topics instead of specific words/phrases to get an idea of what a document is about.
This is where BERTopic comes in! BERTopic is a cutting-edge methodology that leverages the transformers defining the base BERT technique along with other ML tools to provide a flexible and powerful topic modeling module (with great visualization support as well!)
In this notebook, we’ll go through the operation of BERTopic’s key functionalities and present resources for further exploration.
Required installs:#
# Installs the base bertopic module:
!pip install bertopic
# If you want to use other transformers/language backends, it may require additional installs:
!pip install bertopic[flair] # can substitute 'flair' with 'gensim', 'spacy', 'use'
# bertopic also comes with its own handy visualization suite:
!pip install bertopic[visualization]
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zsh:1: no matches found: bertopic[flair]
zsh:1: no matches found: bertopic[visualization]
Data sourcing#
For this exercise, we’re going to use a popular data set, ‘20 Newsgroups,’ which contains ~18,000 newsgroups posts on 20 topics. This dataset is readily available to us through Scikit-Learn:
import bertopic
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
documents = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']
print(documents[0]) # Any ice hockey fans?
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Input In [2], in <cell line: 1>()
----> 1 import bertopic
2 from bertopic import BERTopic
3 from sklearn.datasets import fetch_20newsgroups
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/bertopic/__init__.py:1, in <module>
----> 1 from bertopic._bertopic import BERTopic
3 __version__ = "0.11.0"
5 __all__ = [
6 "BERTopic",
7 ]
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/bertopic/_bertopic.py:32, in <module>
30 from bertopic._utils import MyLogger, check_documents_type, check_embeddings_shape, check_is_fitted
31 from bertopic._mmr import mmr
---> 32 from bertopic.backend._utils import select_backend
33 from bertopic import plotting
35 # Visualization
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/bertopic/backend/__init__.py:2, in <module>
1 from ._base import BaseEmbedder
----> 2 from ._word_doc import WordDocEmbedder
3 from ._utils import languages
5 __all__ = [
6 "BaseEmbedder",
7 "WordDocEmbedder",
8 "languages"
9 ]
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/bertopic/backend/_word_doc.py:4, in <module>
2 from typing import List
3 from bertopic.backend._base import BaseEmbedder
----> 4 from bertopic.backend._utils import select_backend
7 class WordDocEmbedder(BaseEmbedder):
8 """ Combine a document- and word-level embedder
9 """
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/bertopic/backend/_utils.py:2, in <module>
1 from ._base import BaseEmbedder
----> 2 from ._sentencetransformers import SentenceTransformerBackend
3 from ._hftransformers import HFTransformerBackend
4 from transformers.pipelines import Pipeline
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/bertopic/backend/_sentencetransformers.py:3, in <module>
1 import numpy as np
2 from typing import List, Union
----> 3 from sentence_transformers import SentenceTransformer
5 from bertopic.backend import BaseEmbedder
8 class SentenceTransformerBackend(BaseEmbedder):
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/sentence_transformers/__init__.py:3, in <module>
1 __version__ = "2.2.2"
2 __MODEL_HUB_ORGANIZATION__ = 'sentence-transformers'
----> 3 from .datasets import SentencesDataset, ParallelSentencesDataset
4 from .LoggingHandler import LoggingHandler
5 from .SentenceTransformer import SentenceTransformer
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/sentence_transformers/datasets/__init__.py:1, in <module>
----> 1 from .DenoisingAutoEncoderDataset import DenoisingAutoEncoderDataset
2 from .NoDuplicatesDataLoader import NoDuplicatesDataLoader
3 from .ParallelSentencesDataset import ParallelSentencesDataset
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/sentence_transformers/datasets/DenoisingAutoEncoderDataset.py:1, in <module>
----> 1 from torch.utils.data import Dataset
2 from typing import List
3 from ..readers.InputExample import InputExample
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/torch/__init__.py:861, in <module>
859 import torch.utils.backcompat
860 from torch import onnx as onnx
--> 861 from torch import jit as jit
862 from torch import linalg as linalg
863 from torch import hub as hub
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/torch/jit/__init__.py:22, in <module>
9 # These are imported so users can access them from the `torch.jit` module
10 from torch._jit_internal import (
11 Final,
12 Future,
(...)
20 unused,
21 )
---> 22 from torch.jit._script import (
23 script,
24 Attribute,
25 ScriptModule,
26 script_method,
27 RecursiveScriptClass,
28 RecursiveScriptModule,
29 ScriptWarning,
30 interface,
31 CompilationUnit,
32 ScriptFunction,
33 _ScriptProfile,
34 _unwrap_optional,
35 )
36 from torch.jit._trace import (
37 trace,
38 trace_module,
(...)
48 _get_trace_graph,
49 )
50 from torch.jit._async import fork, wait
File ~/.local/share/virtualenvs/SSDS-TAML-xaUfvlpM/lib/python3.9/site-packages/torch/jit/_script.py:39, in <module>
36 from torch.overrides import (
37 has_torch_function, has_torch_function_unary, has_torch_function_variadic)
38 from torch.package import PackageExporter, PackageImporter
---> 39 from ._serialization import validate_map_location
41 from torch.jit._monkeytype_config import (
42 monkeytype_trace,
43 JitTypeTraceConfig ,
44 JitTypeTraceStore
45 )
46 from torch._classes import classes
File <frozen importlib._bootstrap>:1007, in _find_and_load(name, import_)
File <frozen importlib._bootstrap>:986, in _find_and_load_unlocked(name, import_)
File <frozen importlib._bootstrap>:680, in _load_unlocked(spec)
File <frozen importlib._bootstrap_external>:846, in exec_module(self, module)
File <frozen importlib._bootstrap_external>:941, in get_code(self, fullname)
KeyboardInterrupt:
Creating a BERTopic model:#
Using the BERTopic module requires you to fetch an instance of the model. When doing so, you can specify multiple different parameters including:
language-> the language of your documentsmin_topic_size-> the minimum size of a topic; increasing this value will lead to a lower number of topicsembedding_model-> what model you want to use to conduct your word embeddings; many are supported!
For a full list of the parameters and their significance, please see https://github.com/MaartenGr/BERTopic/blob/master/bertopic/_bertopic.py.
Of course, you can always use the default parameter values and instantiate your model as
model = BERTopic(). Once you’ve done so, you’re ready to fit your model to your documents!
Example instantiation:#
from sklearn.feature_extraction.text import CountVectorizer
# example parameter: a custom vectorizer model can be used to remove stopwords from the documents:
stopwords_vectorizer = CountVectorizer(ngram_range=(1, 2), stop_words='english')
# instantiating the model:
model = BERTopic(vectorizer_model = stopwords_vectorizer)
Fitting the model:#
The first step of topic modeling is to fit the model to the documents:
topics, probs = model.fit_transform(documents)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
.fit_transform()returns two outputs:topicscontains mappings of inputs (documents) to their modeled topic (alternatively, cluster)probscontains a list of probabilities that an input belongs to their assigned topic
Note:
fit_transform()can be substituted withfit().fit_transform()allows for the prediction of new documents but demands additional computing power/time.
Viewing topic modeling results:#
The BERTopic module has many built-in methods to view and analyze your fitted model topics. Here are some basics:
# view your topics:
topics_info = model.get_topic_info()
# get detailed information about the top five most common topics:
print(topics_info.head(5))
Topic Count Name
0 -1 6646 -1_file_use_need_using
1 0 1838 0_team_games_players_season
2 1 616 1_clipper_encryption_chip_nsa
3 2 527 2_cheek ken_ken huh_ignore art_huh ignore
4 3 452 3_israel_israeli_jews_palestinian
When examining topic information, you may see a topic with the assigned number ‘-1.’ Topic -1 refers to all input outliers which do not have a topic assigned and should typically be ignored during analysis.
Forcing documents into a topic could decrease the quality of the topics generated, so it’s usually a good idea to allow the model to discard inputs into this ‘Topic -1’ bin.
# access a single topic:
print(model.get_topic(topic=0)) # .get_topics() accesses all topics
[('team', 0.007645058778587724), ('games', 0.006112662299637617), ('players', 0.005412026399964582), ('season', 0.005342811826876292), ('hockey', 0.005239065199444112), ('league', 0.004280045353200042), ('teams', 0.003990602953367509), ('baseball', 0.0037812052034601833), ('nhl', 0.003514144827427642), ('gm', 0.0029900018153221084)]
# get representative documents for a specific topic:
print(model.get_representative_docs(topic=0)) # omit the 'topic' parameter to get docs for all topics
["\ni have no idea, nor do i care. however, i'd like to point out that\nblomberg got the first plate appearance by a designated hitter, and\nthe first walk by a designated hitter. i am not sure, but i do not\nthink that he also got the first hit by a designated hitter.", ": >\n: >ATLANTIC DIVISION\n: >\t\n: >\tST JOHN'S MAPLE LEAFS VS MONCTON HAWKS\n: >\tMONCTON HAWKS\n: >See CD Islanders. Moncton is a very similar team to CDI. Low scoring,\n: >defensive, good goaltending. John Leblanc and Stu Barnes are the only\n: >noticable guns on the team. But the defense is top notch and \n: >Mike O'Neill is the most underrated goalie in the league.\n: >\n\n: Bri, as I have tried to tell you since 2 February, Michael O'Neill\n: might be the most underrated goalie in the AHL, but he ISN'T in the\n: AHL. He's on the Winnipeg Jets' injury list, as he has been since\n: his first NHL start against the Ottawa Senators. He's out until\n: next year after surgery to repair a shoulder separation.\n\n: Stu Barnes might be an AHL gun for the Hawks, but he's now the third\n: line center with the Jets, and has been since mid January or so.\n\nSorry, my memory is gone. I thought that O'Neill got sent back\ndown in February but I must have been given incorrect info. I guess\nthis says it all about Moncton because Barnes is still one of\ntheir top 3 or so scorers even though he's been out since January.", "\n\nI didn't see any smilies in this message so.......\n\n W T L PTs\n Team A 50 30 4 104\n Team B 52 32 0 104\n\n\nThere you go. Two teams that tie in points without identical records.\n\n"]
# find topics similar to a key term/phrase:
topics, similarity_scores = model.find_topics("sports", top_n = 5)
print("Most common topics:" + str(topics)) # view the numbers of the top-5 most similar topics
# print the initial contents of the most similar topics
for topic_num in topics:
print('\nContents from topic number: '+ str(topic_num) + '\n')
print(model.get_topic(topic_num))
Most common topics:[0, 30, 6, 166, 4]
Contents from topic number: 0
[('team', 0.007645058778587724), ('games', 0.006112662299637617), ('players', 0.005412026399964582), ('season', 0.005342811826876292), ('hockey', 0.005239065199444112), ('league', 0.004280045353200042), ('teams', 0.003990602953367509), ('baseball', 0.0037812052034601833), ('nhl', 0.003514144827427642), ('gm', 0.0029900018153221084)]
Contents from topic number: 30
[('games', 0.03260548961663573), ('sega', 0.02366315012814771), ('arcade', 0.012166539858844822), ('snes', 0.010883627526511617), ('sega genesis', 0.01081910740506706), ('joysticks', 0.010294764495945618), ('games sale', 0.010085068481475858), ('sale', 0.00964091677280479), ('joystick', 0.009006639792149954), ('sega cd', 0.0074012373591723)]
Contents from topic number: 6
[('riding', 0.011792240692170709), ('ride', 0.011256591323418531), ('driving', 0.007418204752466058), ('road', 0.007362304673149508), ('traffic', 0.006971330162717447), ('roads', 0.005093305390738552), ('bikes', 0.0046328368271995445), ('bikers', 0.0041220512073587194), ('riders', 0.0037367046265679754), ('passengers', 0.0035386604055364823)]
Contents from topic number: 166
[('religion', 0.024810151190057972), ('war', 0.01958713595572545), ('wars', 0.0141305144151792), ('crusades', 0.012827683749926261), ('history', 0.01202363443416338), ('religious', 0.009458363539211138), ('unbelievers', 0.008338773663764506), ('yoked unbelievers', 0.007970064155940823), ('statement religion', 0.007495172035922859), ('gods', 0.0071255212864334274)]
Contents from topic number: 4
[('health', 0.0072259305085357), ('cancer', 0.005975505039095839), ('disease', 0.00513078203584376), ('tobacco', 0.005069613472607038), ('medical', 0.00492433353954727), ('hiv', 0.004709304265420622), ('malaria', 0.004112010029452724), ('smokeless tobacco', 0.004033769948845448), ('lyme', 0.003923377448522405), ('medical newsletter', 0.003903230753928965)]
Saving/loading models:#
One of the most obvious drawbacks of using the BERTopic technique is the algorithm’s run-time. But, rather than re-running a script every time you want to conduct topic modeling analysis, you can simply save/load models!
# save your model:
# model.save("TAML_ex_model")
# load it later:
# loaded_model = BERTopic.load("TAML_ex_model")
Visualizing topics:#
Although the prior methods can be used to manually examine the textual contents of topics, visualizations can be an excellent way to succinctly communicate the same information.
Depending on the visualization, it can even reveal patterns that would be much harder/impossible to see through textual analysis - like inter-topic distance!
Let’s see some examples!
# Create a 2D representation of your modeled topics & their pairwise distances:
model.visualize_topics()
# Get the words and probabilities of top topics, but in bar chart form!
model.visualize_barchart()
# Evaluate topic similarity through a heat map:
model.visualize_heatmap()
Conclusion#
Hopefully you’re convinced of how accessible but powerful a technique BERTopic topic modeling can be! There’s plenty more to learn about BERTopic than what we’ve covered here, but you should be ready to get started!
During your adventures, you may find the following resources useful:
Original BERTopic Github: https://github.com/MaartenGr/BERTopic
BERTopic visualization guide: https://maartengr.github.io/BERTopic/getting_started/visualization/visualization.html#visualize-terms
How to use BERT to make a custom topic model: https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6
Recommended things to look into next include:
how to select the best embedding model for your BERTopic model;
controlling the number of topics your model generates; and
other visualizations and deciding which ones are best for what kinds of documents.
Questions? Please reach out! Anthony Weng, SSDS consultant, is happy to help (contact: ad2weng@stanford.edu)
Exercise#
Repeat the steps in this notebook with your own data. However, real data does not come with a
fetchfunction. What importation steps do you need to consider so your own corpus works?